The Role of Artificial Intelligence in Optimising Renewable Energy Systems

The integration of Artificial Intelligence (AI) in renewable energy systems has become a revolutionary force optimising energy production to predicting demand and improving grid management, particularly in wind farms. AI driven solutions are transforming the operational landscape of wind farms, resulting in heightened efficiency and substantial cost. A recent study by the National Renewable Energy Laboratory (NREL) has yielded significant insights, showcasing the development of a distributed optimisation framework was developed to maximise the total average power output of wind farms.

According to a report by Verdantix, AI is witnessing widespread adoption across renewable energy sector, spanning applications such as operational excellence, emission reduction, performance optimisation, energy production forecasting, waste management, and recycling. For instance, AI powered automation is enabling a more decentralised, electrified, and green energy system based on renewables such as solar photovoltaics, hydroelectric power, and wind. Google and DeepMind have collaborated to develop a Neural network that enhances the accuracy of wind power output forecasts, allowing the company to sell its power in advance and improve the business case for wind power. Similarly, IBM's program for the US Department of Energy's SunShot Initiative has improved solar forecasting by 30%, leading to cost savings and heightened efficiency.

Furthermore, AI driven optimisation algorithms are playing a pivotal role in enhancing the efficiency of wind farms. Through continuous monitoring and adjustment of turbine settings in response to dynamic conditions, AI algorithms can maximise energy output while minimising operational costs. Through adaptive control strategies, such as model predictive control and reinforcement learning, AI optimises turbine performance by factoring variables such as wind speed, direction, and turbine health.

This framework represents the wind farm as a directed network, with each turbine as a node and aerodynamic interactions between turbines as edges. The weights of these edges are determined by the distance downstream, area overlap based on thresholding, and operating points of the upstream turbines. The optimisation problem is then solved using distributed optimisation algorithms, significantly reducing computation time. Furthermore, surrogate modelling in wind power flow systems has been shown to improve aerodynamic performance. By increasing the thickness of the airfoil, better aerodynamic performance is observed, leading to improved energy efficiency. In addition to these advancements, a new method has been developed to boost wind farms energy output without requiring new equipment.

This method, which involves a predictive wind farm model, can predict potential gains for a given site. By reducing wake losses, the algorithm can make it possible to place turbines more closely together, increasing the power density of wind energy and saving on land or sea footprints.

In Sri Lanka, the International Finance Corporation (IFC) has been working on creating markets in the country, which includes the renewable energy sector. It is clear that AI is being used globally to optimise renewable energy systems.

At Windforce, we are committed to staying at the forefront of these advancements. Our team of experts are dedicated to exploring the latest AI driven solutions to optimise renewable energy systems, including wind farms. By leveraging these technologies, we aim to provide our clients with the most efficient and cost effective solutions in the market. In conclusion, the integration of AI in renewable energy systems is a significant step forward in achieving pressing greenhouse gas emission reduction goals. By optimizing wind farm performance, we can increase power output, reduce costs, and contribute to a more sustainable future. As we continue to innovate and leverage technological advancements, the role of AI in optimising renewable energy systems will only grow in significance, driving us towards a greener and more sustainable tomorrow.